{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f20a920-5c85-4521-8747-ce735797a86c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "plt.style.use('ggplot')\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "# 忽略所有警告\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b145823-ab76-495d-81da-62a47888e4a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=pd.read_excel('result1_1.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "30e67238-620f-4ad2-befc-f9be597de5a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2=pd.read_excel('result1_3.xlsx')\n",
    "data2=data2[data2['生产线']=='M101']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b1014b21-defa-4b2e-bfe4-7f3032165da5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2_count=data2.pivot(index=('月份', '日期'),columns='故障类别',values='总次数').reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9178d616-5f8a-4dc7-8253-8706f230cc9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2_count['总次数']=data2_count.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2ff022ba-c72d-4edb-a051-86fdf00afd37",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2_count.columns=['次数-A1', '次数-A2', '次数-A3', '次数-A4', '总次数']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "64b96763-c69a-43ee-9bc8-f832fcfcb1ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2['时长']=data2['总次数']*data2['平均持续时长（秒）']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0bac20c8-c661-4a5f-ae21-47e667840940",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2_time=data2.pivot(index=('月份', '日期'),columns='故障类别',values='时长').reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ef29f86f-cdeb-485f-9148-d7e1066c96d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2_time['总时长']=data2_time.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "28c96210-d6e2-4825-acab-f8d22cb62d53",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2_time.columns=['时长-A1', '时长-A2', '时长-A3', '时长-A4', '总时长']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f351b7a3-f99f-4da6-983f-67f4d04d52ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "data3=pd.read_excel('result1_4.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "962182b7-e7d6-4fdc-b894-7d7dc4dcaf2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.concat([data1,data2_count,data2_time,data3.iloc[:,2]],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "29346adc-adff-40be-8992-b9aa2b6580b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr=data.fillna(0).corr().iloc[6:,[2,5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "32a42fa3-76d1-422b-a142-b67a0a67efaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(corr, annot=True, fmt=\".1f\", cmap='coolwarm')\n",
    "# 设置标题\n",
    "plt.title('相关性矩阵热力图')\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "93e2433d-023f-4153-b820-0f958a7a513f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def train(X,y):\n",
    "    # 划分训练集和测试集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "    # 创建随机森林回归器实例\n",
    "    model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "    # 训练模型\n",
    "    model.fit(X_train, y_train)\n",
    "    # 预测测试集\n",
    "    y_pred = model.predict(X_test)\n",
    "    # 计算均方误差（MSE）\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "    # 获取特征重要性\n",
    "    feature_importances = model.feature_importances_\n",
    "    # 将特征重要性与特征名称结合起来\n",
    "    features = X.columns\n",
    "    importance_df1 = pd.DataFrame({'Feature': features, 'Importance': feature_importances})\n",
    "    # 对特征重要性进行排序\n",
    "    importance_df1 = importance_df1.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "    return mse,importance_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "73545ada-1e62-4144-8ace-951f7a7f4dc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "mse1,df1=train(data.iloc[:,6:],data[['合格率（%）']])\n",
    "mse2,df2=train(data.iloc[:,6:],data[['产品总数（件）']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "2c153952-e18a-40cb-ae00-445cb7585d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance</th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>时长-A4</td>\n",
       "      <td>0.173797</td>\n",
       "      <td>时长-A4</td>\n",
       "      <td>0.000171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>时长-A2</td>\n",
       "      <td>0.163918</td>\n",
       "      <td>时长-A2</td>\n",
       "      <td>0.000327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>时长-A3</td>\n",
       "      <td>0.160991</td>\n",
       "      <td>时长-A3</td>\n",
       "      <td>0.000081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>时长-A1</td>\n",
       "      <td>0.153762</td>\n",
       "      <td>时长-A1</td>\n",
       "      <td>0.000166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M101（小时）</td>\n",
       "      <td>0.115006</td>\n",
       "      <td>M101（小时）</td>\n",
       "      <td>0.528046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>总时长</td>\n",
       "      <td>0.099598</td>\n",
       "      <td>总时长</td>\n",
       "      <td>0.469054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>总次数</td>\n",
       "      <td>0.049127</td>\n",
       "      <td>总次数</td>\n",
       "      <td>0.001707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>次数-A2</td>\n",
       "      <td>0.031907</td>\n",
       "      <td>次数-A2</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>次数-A4</td>\n",
       "      <td>0.027944</td>\n",
       "      <td>次数-A4</td>\n",
       "      <td>0.000069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>次数-A3</td>\n",
       "      <td>0.012572</td>\n",
       "      <td>次数-A3</td>\n",
       "      <td>0.000044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>次数-A1</td>\n",
       "      <td>0.011378</td>\n",
       "      <td>次数-A1</td>\n",
       "      <td>0.000207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature  Importance   Feature  Importance\n",
       "8      时长-A4    0.173797     时长-A4    0.000171\n",
       "6      时长-A2    0.163918     时长-A2    0.000327\n",
       "7      时长-A3    0.160991     时长-A3    0.000081\n",
       "5      时长-A1    0.153762     时长-A1    0.000166\n",
       "10  M101（小时）    0.115006  M101（小时）    0.528046\n",
       "9        总时长    0.099598       总时长    0.469054\n",
       "4        总次数    0.049127       总次数    0.001707\n",
       "1      次数-A2    0.031907     次数-A2    0.000129\n",
       "3      次数-A4    0.027944     次数-A4    0.000069\n",
       "2      次数-A3    0.012572     次数-A3    0.000044\n",
       "0      次数-A1    0.011378     次数-A1    0.000207"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d478232f-0a9d-485b-a9e4-c71de055047f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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